{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GD Regression.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "d1eijtJishCs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt \n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "from sklearn import datasets,linear_model, metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CPM_IXjsshC7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "boston = datasets.load_boston()\n",
        "X = boston.data \n",
        "y = boston.target\n",
        "# X=pd.DataFrame(X)\n",
        "# print(X)\n",
        "# y=pd.DataFrame(y)\n",
        "# print(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BtT5OsE5shDD",
        "colab_type": "code",
        "colab": {},
        "outputId": "c70ddbd4-af98-47a6-8e07-af281508c08b"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4,random_state=1) \n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(303, 13)\n",
            "(303,)\n",
            "(203, 13)\n",
            "(203,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HejNzeV7shDT",
        "colab_type": "code",
        "colab": {},
        "outputId": "d5d20cc5-f3eb-49bc-ce74-6b3dfa7c708e"
      },
      "cell_type": "code",
      "source": [
        "reg = linear_model.SGDRegressor(max_iter=8000,alpha=10,loss='huber',shuffle=True)\n",
        "reg.fit(X_train, y_train)\n",
        "print('Coefficients: \\n', reg.coef_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: \n",
            " [-0.00029299  0.04176487 -0.00545658  0.00029148  0.00021499  0.00571752\n",
            "  0.00099275  0.00253648 -0.001752    0.0195632   0.00330779  0.04352452\n",
            " -0.01196262]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7EkTxPz4shDe",
        "colab_type": "code",
        "colab": {},
        "outputId": "39342097-0d71-4db5-908e-71d9e8a23327"
      },
      "cell_type": "code",
      "source": [
        "print('Variance score: ',reg.score(X_test, y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variance score:  0.0686430245080688\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yhmzq-uAshDm",
        "colab_type": "code",
        "colab": {},
        "outputId": "b28a7b94-f748-4253-96e0-86d5316b7b89"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, \n",
        "            color = \"green\", s = 10, label = 'Train data') \n",
        "  \n",
        " \n",
        "plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, \n",
        "            color = \"blue\", s = 10, label = 'Test data') \n",
        "  \n",
        " \n",
        "plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) \n",
        "\n",
        "plt.legend(loc = 'upper right')  \n",
        "plt.title(\"Residual errors\") \n",
        "plt.show() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}